System and method for enhancing the accuracy of a location estimate

ABSTRACT

A method for enabling a system to enhance the accuracy of a location estimate modifies weights in a weight matrix associated with receiver station measurements in parallel with successive refinements of the location estimate. In a typical location estimation scenario, several receiving stations simultaneously derive measurements of a signal from the emitter. Any one of these measurements is in general some function of the emitter location and the receiving station location. The aggregate of these measurements is often in excess of the minimum number of measurements required to provide an estimate of the emitter location. Where such an excess exists, the method proceeds by modifying the weights associated with the measurements in parallel with successive refinements of the location estimate. The method can be implemented over various cellular protocols with a consistent and significant enhancement in the accuracy of location estimates.

CROSS REFERENCES

The present application is a continuation of and is co-pending withnon-provisional application entitled, “System and Method for Enhancingthe Accuracy of a Location Estimate,” application Ser. No. 10/531,044,filed Oct. 19, 2005, which claims the priority benefit of InternationalApplication Number PCT/US2003/032584 and claims priority benefit ofprovisional application entitled “Geolocation of Mobile Appliances”,Appl. Ser. No. 60/418,342 and filed on Oct. 16, 2002, the entirety ofwhich is hereby incorporated herein by reference.

The present application is related to and concurrently filed withapplications titled “A NETWORK OVERLAY GEO-LOCATION SYSTEM WITH SMARTANTENNAS AND METHOD OF OPERATION” Ser. No. 10/531,040, “WIRELESSCOMMUNICATION NETWORK MEASUREMENT DATA COLLECTION USING INFRASTRUCTUREOVERLAY-BASED HANDSET LOCATION SYSTEMS” Ser. No. 10/531,042, “NETWORKOVERLAY LOCATION SYSTEM AND METHOD FOR AIR INTERFACE WITH FREQUENCYHOPPING” Ser. No. 10/531,041, “A SYSTEM AND METHOD FOR ESTIMATING THEMULTI-PATH DELAYS IN A SIGNAL USING A SPATIALLY BLIND ANTENNA ARRAY,Ser. No. 10/531,039, and “SYSTEM AND METHOD FOR OPERATING A NETWORKOVERLAY GEO-LOCATION SYSTEM WITH REPEATERS” Ser. No. 10/531,038, eachfiled Oct. 16, 2003, the entirety of each of these applications isincorporated herein by reference.

BACKGROUND

In a typical location estimation scenario, several receiving stationssimultaneously derive measurements on the emitter signal, the emitterbeing a wireless transmitter, a mobile appliance such as a mobile phone,Personal Digital Assistant (“PDA”), or personal computer with wirelesscapability. Ideally, any one of these measurements is a function only ofthe emitter location {right arrow over (P)}, the receiving stationlocations {right arrow over (S)}_(j) (where the subscript j denotes thestation), and the antenna configurations at the receiving stations. Agiven receiving station may attempt to derive the bearing or Angle ofArrival (“AOA”) at which the emitter is located. A different receivingstation may attempt to derive the Time Of Arrival (“TOA”) of theemitter's signal. Some receiving station pairs may attempt to derive theTime Difference Of Arrival (“TDOA”) of the emitter signal between thestation pair. Other receiving station pairs may attempt to compute theFrequency Difference Of Arrival (“FDOA”) of the emitter signal betweenthe station pair. The form of the measurements is not restricted to theabove; certain receiving stations may derive a multiplicity of themeasurements indicated or other more exotic measurements.

Most prior art location estimating systems have more receiving stationsin place than minimally required. For example, if all receiving stationswere to use TOAs to determine the emitter position, three stations wouldsuffice in the ideal case. For a perfect estimate in this scenario, thethree TOAs will be exactly correct (perfect) measurements. For receivingstation using AOA, two stations would suffice in the ideal case. Wherethe number of receiving stations used in the estimate is above the idealnumber, the estimate becomes an overdetermined solution. The fact thatsuch perfect measurements are never available in practice necessitatesthe use of an excess of receiving stations and a resulting excess ofmeasurements and an overdetermined solution. Given the excessmeasurements, a location estimate is derived to best fit themeasurements.

In a mixed mode system, a combination of TOAs, TDOAs, AOAs, FDOAs andother measurements are combined to estimate the emitter position.Whether mixed mode or not, the same principle of using (or attempting touse) an excess of receiving stations is applied to generate a morereliable estimate of the emitter location.

In most prior art systems, the mathematical approach taken to derive alocation from such an excess of measurements assumes that each of themeasurements is the perfect measurement corrupted with Guassian noisewith some known statistics. A more detailed explanation of this approachcan be found in M. Wax, “Position location from sensors with positionuncertainty”, IEEE Trans. Aero., Elect. Syst. AES-19, no. 2 (September1983), 658-662; D. J. Torrieri. “Statistical Theory of Passive LocationSystems”, IEEE Trans. Aerosp. Electron. Syst. AES-20, no. 2 (March1984), 183-198; Y. T. Chan and K. C. Ho, “A simple and efficientestimator for hyperbolic location”, IEEE Trans. Signal Proc. 42, no. 8(August 1994), 1905-1915; W. H. Foy. “Position location solutions byTaylor series estimation”, IEEE trans Aerosp. Electron. System AES-12,no. 2 (March 1976), 187-194; R. G. Stansfield, “Statistical theory of DFfixing”, Journ. IEE 94, part IIIa (October 1947), 762-770; the entiretyof each is herein incorporated by reference. This technique has a longand established history and serves as the bedrock of locationestimation.

In actual systems, modeling the receiver station measurement as aperfect signal corrupted by noise is accurate only in a small minorityof cases. The reason for this is that measurements (of any of the typesindicated earlier) typically have biases which are rarely (if ever)reflected in the noise statistics. This measurement bias may stem from avariety of factors. One source of bias is instrumentation error. Anothersource of bias is signal multipath where a delayed signal, a reflectionof the original signal, masquerades as the desired signal.

In determining a location estimate, it is convenient to associate aweight with each measurement. This translates mathematically to eitherindividual weights or a matrix that expresses inter-relationships amongthe measurements. Given that the biases are unknown and may (in the caseof multipath) be functions of the emitter location P, a directmathematical solution using both the initial weights and unknown biasesis impossible: there are an infinite number of solutions. On the otherhand, all known solution techniques that ignore the measurement biasresult in an estimate that is itself biased. Since the location estimateis derived by a mathematical weighting of each measurement, the weightsapplied to each measurement have a strong effect on the error in thelocation estimate. Ideally, the weights applied to good measurementsshould always be larger than those applied to biased measurements.

Several prior art attempts have been made to address the issue of biasedmeasurements and are described in detail in M. P. Wylie and J. Houtzman,“The non-line of sight problem in mobile location estimation”. Proc.IEEE 5^(th) International Conf. on Universal Personal Communications,vol. 2 (October 1996), 827-831; L. Cong and W. Xuang, “Non-Line-of-SightError Mitigation in TDOA mobile location” Proc. IEEE GlobalTelecommunications conference vol. 1 (2001), 680-684; P. C. Chen, “Anon-line-of-sight error mitigation algorithm in location estimating”Proc. IEEE Conf. on wireless Communications Networking, vol. 1 (1999),316-320; and N. J. Thomas, D. G. M. Cruickshank and D. I. Laurenson,“Performance of a TDOA-AOA hybrid mobile location system” 3G MobileCommunication Technologies Conf Proc. 1 (March 2001), 216-220, all ofwhich are incorporated herein by reference. These references describeapproaches that identify the offending measurements and then eithereliminate such measurements or model the offending measurements with adistribution different from the traditional approach. Some of thesemethods additionally require a large number of samples of a particularmeasurement to create a time-history of the measurement.

A major problem with the prior art techniques is that in practicalsystems the differentiation between biased and non-biased measurementsis never clear-cut. Unlike in purely academic simulations, real lifedata reveals a continuum ranging from near perfect measurements tomeasurements with large biases. Experimentation with schemes thatattempt to isolate a particular biased measurement have shown that suchschemes rarely work. These approaches have great difficulty inidentifying the offending measurements when more than one receivingstation is in error.

For purposes of this disclosure, dominant measurements are thosemeasurements that most strongly influence the location estimate. Whenthe dominant measurements have a smaller bias than the remainingmeasurements, the estimate generated by any of the traditional prior artsolution techniques mentioned previously is a better estimate than theestimate generated by the measurements with larger bias. That is, theestimate is far from perfect, but is skewed to favor the dominantmeasurements that within this scenario are described as having a smallbias. In practice, and especially when a fair excess of measurements isavailable, the dominant measurements actually have a smaller bias.

One reason for this is that the set of low bias measurements have agreater degree of self-coherence (less variation or greater mutualagreement) with respect to the mathematics that generates the estimate;hence, they are more likely to dominate the estimate. The skew in theestimate makes the non-dominant measurements have a larger offset withrespect to the estimate than the dominant measurements. It is importantto note that one or more non-dominant measurements may have a largeweight. The self-coherence of several lower weight measurements can thusdominate the location estimate, over-riding the non-dominant high weightmeasurement that is possibly in error.

The disclosed subject matter capitalizes on this fact to adjust theweights applied to the measurements by the offsets from the hypotheticalmeasurements, with the assumption that at each iteration the locationestimate is exact. Thus, the relative weights applied to the dominantmeasurements are increased with respect to the non-dominantmeasurements. This process is recursively refined to generate furtherimprovements in the location estimate. The dominant measurements may ormay not be a majority of the measurements since only a few high weightmeasurements may dominate the estimate. Conversely several lower weightmeasurements may prove dominant in terms of the location estimate, andhence their weights may increase in the next iteration of the process,while the weights applied to the other measurements may decrease.

The method disclosed makes no attempt to determine the offendingmeasurements. The goal, rather, is to improve the location estimate.However, the method can be used to identify the biased measurementssubsequent to refining the location estimate.

Therefore, it is an object of the present subject matter to obviate thedeficiencies of the prior art and present a novel method and system forrecursively refining location estimates by accounting for receiver biaswhere an overdetermined solution exists.

It is also an object of the present subject matter to present animproved method for refining a geo-location estimate of a wirelesstransmitter emitting a signal that is received by a predetermined numberof sensors that is greater than the minimum number of sensors requiredto obtain the geo-location estimate. The improvement may includeincorporating bias error in the signals received at the sensors andupdating the geo-location estimate by recursive analysis of the biaserror to thereby refine the geo-location estimate.

It is still an object of the present subject matter to present a methodfor estimating the geo-location of a wireless transmitter emitting asignal that is received by a plurality of sensors in a geo-locationsystem which includes a geo-location estimation device which provides anoverdetermined geo-location solution for the wireless transmitter. Themethod includes measuring an attribute of the emitted signal to therebycreate a sensor signal at the sensor and sending the sensor signal tothe geo-location estimation device. The method may also includereceiving the plural sensor signals, associating with each sensor signala separate initial predetermined weight value to thereby provide aplurality of initial estimation signals, determining an initial estimateof the geo-location of the wireless transmitter from the initialestimation signals, and modifying the weight value associated with thedominant sensor signals relative to the weight value associated with thenon-dominant sensor signals to thereby provide a plurality of refinedestimation signals.

It is another object of the present subject matter to present a novelmethod for estimating the geo-location of a wireless transmitteremitting a signal that is received by a plurality of sensors in ageo-location system which includes a geo-location estimation devicewhich provides an overdetermined geo-location solution for the wirelesstransmitter as a function of sensor signals determined from an attributeof the received signal at the plurality of sensors.

It is yet another object of the present subject matter to present anovel system for estimating the geo-location of a wireless transmitteremitting a signal that is received by a plurality of sensors in ageo-location system which further includes a geo-location estimationdevice which provides an overdetermined geo-location solution for thewireless transmitter as a function of sensor signals determined from anattribute of the received signal at the plurality of sensors.

These and other advantages of the disclosed subject matter over theprior art will be readily apparent to one skilled in the art to whichthe disclosure pertains from a perusal of the claims, the appendeddrawings, and the following detailed description of the preferredembodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of the elements used to estimate a location ina TOA geolocation system according to an embodiment of the disclosedsubject matter.

FIG. 2 is an illustration of the elements used to estimate a location ina AOA geolocation system according to an embodiment of the disclosedsubject matter.

FIG. 3 is a flow chart representing an embodiment of the disclosedsubject matter.

DETAILED DESCRIPTION

A mathematical description follows to aid in the description of thecurrent subject matter. The set of measurements from the receivingstations is denoted by m. These measurements are determined from anattribute of the received signal from the mobile emitter. The receivingstations are typically sensors co-located with base stations in awireless communication system. The sensors can also be located atrepeater stations within the communication system or remotely located.The elements of this column vector m are the individual measurements,possibly of several different types, such as TOA, TDOA or AOA, FDOA etc.Each component in the vector is associated with one or more of thereceiving stations j, where j denotes (indexes) the station. The selfand inter-relationships between the measurements are denoted by theweight matrix W_(k) where k denotes the iteration number; k is equal tounity at the outset and increases in steps of 1 at each iteration. Thematrix formulation is adopted for simplicity since the actualinter-relationships between the measurements could have a more complexform. In the simplest case, the weight matrix W_(k) is a diagonal matrixexpressing the variance associated with individual measurements.

The emitter location estimate is denoted by {circumflex over (P)}_(k),and the receiving station locations are denoted by the column vector{right arrow over (s)}. The emitter location estimate is given as:{circumflex over (P)} _(k) =f({right arrow over (s)}, m,W _(k));k=1,2,3  (1)Thus the location estimate is a function of receiving station locations,measurements and weights. The subscript k denotes the iteration number.

Here, f ({right arrow over (s)}, m, W_(k)) is the function used togenerate a location estimate given the set of measurements and theirself and inter-relationships. Depending on the approach taken, f ({rightarrow over (s)}, m, W_(k)) could be one of many different functions. Forexample, any one of the functions expressing the location estimate interms of the sensor positions and the measurements as described in theprior art may be used.

It is clear that the established methods encapsulated by equation (1)need to use a particular weight matrix. In our formulation, the initialweight matrix WI, is a known weight matrix which can be predeterminedbased on prior knowledge or attributes of the received signals such asthe Signal to Noise Ratio (SNR), or even in the most trivial case, anidentity matrix. The initial location ({circumflex over (P)}₁) may bederived from an explicit solution such as in the prior art solutionsdiscussed previously or derived using a specific algorithm satisfactoryto the practitioner of this art.

Methods embodying the disclosed subject matter iteratively update ormodify the weight matrix in accordance with:W _(k+1) =g(W _(k) ,{right arrow over (s)}, m,{circumflex over (P)}_(k)); k=1,2,3,  (2)where the function g(W_(k), {right arrow over (S)}, m, {circumflex over(P)}_(k)) modifies the weight matrix depending on the agreement betweenthe individual measurements in m, the receiving station locations S_(j)and the emitter location estimate at the k^(th) step, {circumflex over(P)}_(k).

Equation (2) summarizes the general approach embodied by the disclosedsubject matter. To provide more detail for a particular case, anembodiment of a location system using only TOAs is illustrated.

FIG. 1 is a representation of a geolocation system using TOA, showingthe respective elements of equation (1). The receiving stations aredesignated as 101-104, the actual mobile emitter location {right arrowover (P)} is shown as 110 while the estimated mobile emitter location atrecursion or iteration k, {right arrow over (P)}_(k), is shown as 111.

Let the sensor locations be denoted by S_(j), where j=1, 2 . . . N; Ndenoting the number of sensors; in FIG. 1, N=4.

An embodiment of equation (2) for this case is then:W _(k+1) =W _(k)+α( m−t _(k))( m−t _(k))^(T),  (3)in whicht _(k)=(t _(1,k) t _(2,k) . . . t _(N,k))^(T)=(t _(1,k) t _(2,k) . . . t_(4,k))^(T)

where “T” denotes matrix transposition,

t_(j, k) = S_(j) − P̂_(k),

and α is a constant matrix which in its simplest form is a constantscalar times an identity matrix and can be theoretically or empiricallydetermined or provided by the geo-location system operator as a fixedsetting or in real time.

As a second illustration of the method, consider a scheme that uses onlyTDOAs. In this case a possible embodiment of equation (2) isW _(k+1) =W _(k)+α( m−τ _(k))( m−τ _(k))^(T)  (4)

where each element in m denotes the TDOA measurements with respect to aparticular receiving station pair,τ_(k)=Ht_(k)

the (N−1)×(N) matrix H is given by:

$\begin{bmatrix}1 & 0 & 0 & \ldots & 0 & {- 1} \\0 & 1 & 0 & \ldots & 0 & {- 1} \\\ldots & \ldots & \ldots & \ldots & \ldots & \ldots \\0 & 0 & 0 & \ldots & 1 & {- 1}\end{bmatrix}\quad$and the same reference station (station N in this case) is used in theformation of the TDOA measurements.

A third embodiment of equation (2) is shown in FIG. 2, where thelocation estimation scheme uses AOA. In this case a possible embodimentof equation (2) isW _(k+1) =W _(k)+α( m−γ _(k))( m−γ _(k))^(T)  (5)

where each element in m denotes the AOA measurement with respect to aparticular receiving station, and

${\gamma_{kj} = {\tan^{- 1}\left( \frac{\left. {{\hat{P}}_{ky} - S_{jy}} \right)}{\left. {{\hat{P}}_{kx} - S_{jx}} \right)\;} \right)}},$where subscripts x and y indicate the individual components along thex-axis and y-axis in a 2-dimensional coordinate scheme j indexes thestation and k indexes the iteration.

It will be understood by those of skill in the art that although theabove-described embodiments describe systems with four sensors, systemswith other than four sensors are contemplated by the present subjectmatter consistent with the description herein.

FIG. 3 shows a flow chart illustrating a typical implementation of thedisclosed subject matter. Measurements or sensor data, where the latteris the precursor of the former along with an initial Weight matrix aresupplied to block 310 where any of the prior art methods consistent withequation (1) are used to produce an initial fix. The initial fix orlocation estimate {circumflex over (P)}₁ along with the measurements orsensor data are supplied to block 320 where measurement offsets aredetermined. These offsets are used to update the weight matrix accordingto equation (2) in block 330. A new location estimate {circumflex over(P)}_(k) is generated with the updated or modified Weight matrix and themeasurement m in block 340. The iterations continue until either thePosition estimates converge as shown in block 350, or alternatively theiterations cease when some attribute of the Weight matrix converges.

The iteration process can be implemented at the geo-location estimationdevice or in a subsystem in operational communication with thegeo-location device. The geo-location device is generally a processorthat performs the mathematical manipulation of the signals from thesensors. The mathematical manipulation can be accomplished with hardwareand/or software.

The technique of the current subject matter iteratively modifies theweight matrix W_(k) as expressed by equations (1) and (2) is functionalover varying terrain, different receiving station configurations, andover widely varying cellular protocols.

While preferred embodiments of the present inventive system and methodhave been described, it is to be understood that the embodimentsdescribed are illustrative only and that the scope of the embodiments ofthe present inventive system and method is to be defined solely by theappended claims when accorded a full range of equivalence, manyvariations and modifications naturally occurring to those of skill inthe art from a perusal hereof.

1. A method for geo-locating a wireless transmitter emitting a signalcomprising; receiving the signal at a predetermined number of sensors,said predetermined number of sensors being greater than the minimumnumber of sensors required to obtain the geo-location estimate, andsignals from the sensors containing information used to estimate thelocation contain a bias error; determining a geo-location estimate ofthe wireless transmitter based on information from the predeterminednumber of sensors; and updating the geo-location estimate by recursiveanalysis of the bias error to thereby refine the geo-location estimate.2. The method of claim 1 wherein the predetermined number of sensors isfour.
 3. The method of claim 1 wherein the received signals from thesensors are provided at a location estimation device.
 4. The method ofclaim 3 wherein the location estimation device determines thegeo-location of the wireless transmitter by a method selected from thegroup consisting of time of arrival, time difference of arrival,frequency difference of arrival, and angle of arrival.
 5. The method ofclaim 3 wherein the location estimation device determines thegeo-location of the wireless transmitter by a plurality of methodsselected from the group consisting of time of arrival, time differenceof arrival, frequency of arrival, and angle of arrival.
 6. The method ofclaim 1, further comprising determining which one of the sensors has thehighest contribution to the bias error and updating the geo-locationestimate without information from the one sensor.
 7. The method of claim3, wherein the location estimation device is remote from the wirelesstransmitter.